CN110190615A - A kind of microgrid energy-storage system control strategy optimization method - Google Patents
A kind of microgrid energy-storage system control strategy optimization method Download PDFInfo
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Abstract
The invention discloses a kind of microgrid energy-storage system control strategy optimization methods.The present invention is based on the mixed integer linear programming models of roll stablized loop principle building microgrid energy-storage system charge/discharge control strategy, it is minimised as optimal objective with the operating cost of microgrid energy-storage system, establishes the constraint condition of microgrid and the operation control of battery energy storage device；The model is calculated using based on chaos optimization improved symbiont searching algorithm, analyzes the feature of microgrid signal and battery signal under optimal control policy.Present invention applying rolling time domain Dynamic Control Strategy in model construction introduces the optimization method and concept of dynamic prediction, and local optimum is repeated so that prediction result is more nearly global optimization；In addition, use improved symbiont searching algorithm when calculating, faster, and the cost for reaching in the case where maintaining system normal operating level operational objective is minimum, effectively realizes improved efficiency and cost savings for convergence rate.
Description
Technical field
It is specifically a kind of based on improvement symbiont search the invention belongs to microgrid energy-storage system control strategy field
The microgrid energy-storage system control strategy optimization method of algorithm.
Background technique
Following two aspects problem is primarily present for the research of microgrid energy-storage system control strategy at this stage, first is that in reality
In operation, the influence factor of the Optimal Control Strategy of microgrid energy-storage system also changes constantly with the difference of operating status, but
Be current research it is mostly static modelling, cannot achieve dynamic optimization target；Second is that the calculation method that current research is taken is mostly
The traditional algorithms such as genetic algorithm and particle swarm algorithm, are all left to be desired on convergence time and calculated result.
" the Optimization about control parameter method of distributed energy storage system in microgrid ", Central China University of Science and Technology's journal (natural science
Version), 2014,42 (12): 1-5, Zhang Buhan, Chen Yi, Dai Xiaokang, Zhao Shuan, based on system condition of small signal space equation,
A kind of parameter optimization method is proposed for the distributed energy storage system in isolated power grid, it is asymptotic that this method has comprehensively considered system
The factors such as stable, control bandwidth and robustness, have determined the target letter of parameter optimization according to Routh-Hurwitz and L2 gain
Number, and solved using chaotic particle swarm optimization (CPSO) algorithm；But the derivation algorithm that this method uses is more traditional, calculates
As a result it is left to be desired.
" independently operated wind/light/storage micro-grid system energy dynamics optimizing research ", Guangxi University's journal (natural science
Learn), 2013,38 (02): 431-438, Ma Xiaojuan, Lv Zhilin, Lu Ziguang, Hu Likun, Lu Quan, for independently operated wind/
Light/storage complementation micro-grid system proposes a kind of constant dynamic optimization plan in its maximum carrying capacity state of guarantee stored energy capacitance
Slightly, to guarantee the proof load uninterrupted power supply when natural conditions are bad, this method is to maximally utilize wind energy and solar energy is
Principle establishes model with the minimum target of operation cost of electricity-generating of whole system；But the research only accounts for micro-grid system independence
The case where when operation, under grid-connected administrative situation energy flow problem and economic benefit not can relate to.
" the polynary energy storage of isolated microgrid and diesel-driven generator coordination control strategy ", Automation of Electric Systems, 2014,38
(17): 73-79+97, Bi Rui, Wu Jianfeng, Ding Ming, Chen Zhong, Zhu Chengzhi, Zhao Bo, the research real-time dynamic equilibrium of isolated microgrid are asked
Topic is proposed and is according to priority designed after considering the climbing tracking ability in circular flow service life and diesel-driven generator of energy storage
Polynary energy storage and diesel-driven generator coordination strategy can optimize according to the different characteristics of equipment unit and cooperate, make polynary energy storage with
The comprehensive of diesel-driven generator can be promoted；But it equally only considered the state of microgrid isolated operation, energy in the research
Management strategy is not able to achieve the real time dynamic optimization under grid connection state.
Summary of the invention
The technical problem to be solved by the present invention is to overcome the problems of the above-mentioned prior art, provide a kind of based on improvement
The microgrid energy-storage system control strategy optimization method of symbiont searching algorithm constructs the microgrid storage based on roll stablized loop
Energy system control strategy mixed integer linear programming model, is minimised as optimal objective with the operating cost of microgrid energy-storage system,
It establishes microgrid and battery energy storage device runs control constraints；Furthermore, it is also proposed that a kind of to utilize the improved symbiont of chaos optimization
Searching algorithm calculates model using the algorithm, studies the feature of microgrid signal and battery signal under optimal control policy.
For this purpose, the present invention adopts the following technical scheme that: a kind of microgrid energy-storage system control strategy optimization method is based on
Roll stablized loop principle constructs the mixed integer linear programming model of microgrid energy-storage system charge/discharge control strategy, with microgrid
The operating cost of energy-storage system is minimised as optimal objective, establishes the constraint condition of microgrid and the operation control of battery energy storage device；
The model is calculated using based on chaos optimization improved symbiont searching algorithm, is analyzed under optimal control policy
The feature of microgrid signal and battery signal.
Further, optimized by rolling time horizon, the run the period of microgrid energy-storage system is divided, and with step-length
Form carries out rolling simulation and optimization to the operation reserve of microgrid energy-storage system.
Further, rolling window technology is the core of rolling time horizon optimization, and microgrid energy-storage system is in the rolling window time
The local optimum rolled in step delta T, it is assumed that microgrid energy-storage system is reaching office after rolling window time step Δ T
All completion tasks are then moved into completion window from prediction window, then choose several tasks entrance in window from waiting by portion's optimization
Prediction window chooses the rolling optimization that the new rolling window period starts a new round.
Further, rolling time horizon optimization realizes that method is as follows by AEMS: assuming that for each time step,
Algorithm prediction generates one and estimates net demand power, while inputting cost, demand power baseline and time domain starting point in time domain
Actual battery energy, to optimize to obtain battery energy storage device by mixed integer linear programming in each time step
Optimal charge-discharge electric power, and using rolling time horizon optimization method to microgrid energy-storage system the whole service period operation reserve
It is solved.
Further, the objective function of the mixed integer linear programming model of microgrid energy-storage system charge/discharge control strategy
Are as follows:
In formula, T is the time span of optimized variable, the quantity of all rolling time horizon windows during entire model optimization
For N, rolling window time step is Δ T, and the total duration of entire optimization process is NΔT, reach region within the scope of each step-length
The optimization of next time-domain step size is carried out when optimal；3 factors are shared in formula (1), meaning is as follows:
Factor 1 indicates the net electric cost of microgrid energy-storage system, BPcbIt indicates to be purchased from power grid portion in battery energy storage charging
The power divided, kW；Vb TFor the price of microgrid power purchase from power grid, member/kW；BPcsIt indicates to be sold to power grid in battery energy storage charging
Partial power, kW；Vs TIt is energy-storage system to the price of power grid sale of electricity, member/kW；BPdbEnergy-storage system is purchased from when for battery discharge
In the power of power grid, kW；BPdsExpression energy-storage system in battery discharge is sold to the power of grid parts, kW；It should be noted that
It is, as net demand vector power DnetWhen > 0, BPcs=0 namely when demand excess, charge power no longer includes to power grid sale of electricity
Part；As net demand vector power DnetWhen < 0, BPdb=0, namely when demand deficiency, discharge power no longer includes from electricity
The part of online shopping electricity；For entire energy-storage system, realtime power is BP=BPcs+BPcb-BPdb-BPds, BP is canonical
Indicate that total charge power is greater than discharge power, system charge is continuously increased, and on the contrary then discharge power is greater than charge power, system electricity
Amount is constantly reduced；Net demand vector power DnetIndicate the practical difference between demand power and wind power plant generated output, kw；
Factor 2 indicates the use cost and signal smoothing cost of battery, BPcAnd BPdRepresent charge/discharge power, BPc=
BPcs+BPcb, kW；BC is the cost of battery operation, member/kWh；BR is battery energy storage system power ratio in continued time domain step-length
Change magnitude, %；CBRFor the cost of smooth power of battery distribution signal, member；
Factor 3 indicates that microgrid signal forms cost, and GR is the change magnitude of microgrid power in continued time domain step-length, %；CGR
For the cost of smooth microgrid signal, member；D is the power level of whole system, kW, D=BP+DnetNamely the total electricity of system needs
Seek power；DhighFor the maximum power value more than benchmark demand power, kW；ChighFor the cost for cutting down this power, member；DmaxFor
Microgrid maximum demanded power, kW；DminFor microgrid minimum essential requirement power, kW；CflatFor the cost of smooth microgrid demand power, member；
Further, the constraint condition of the microgrid and the operation control of battery energy storage device are as follows: the electricity of microgrid energy-storage system
The constraint of pond energy storage device charge/discharge state decision, battery energy storage system energy and changed power constrains and micro-grid system
Signal bondage.
Further, in battery energy storage device charge/discharge state decision constraint,
The variation range constraint of battery charging and discharging power is as follows:
0≤BPc≤BPc,max·α≤BPc,max (2)
0≤BPd≤BPd,max·(1-α)≤BPd,max (3)
Wherein, α ∈ [0,1] is binary vector, for i-th of element, αi=1 indicates charging, αi=0 indicates electric discharge；
BPc,max, BPd,maxThe respectively maximum value of the specified charge/discharge power of energy-storage system, kW；
The state decision constraint that electricity was bought/sold to microgrid from power grid is as follows:
BPdb,max·(1-θb)≤BPdb≤BPdb,max (4)
BPds,max·(1-θs)≤BPds≤BPds,max (5)
BPcs,max·(1-θs)≤BPcs≤BPcs,max (6)
BPcb,max·(1-θb)≤BPcb≤BPcb,max (7)
0≤θs,θb≤1 (8)
BPk db,max=min (max (0, Pdk),BPd,max) (9)
BPk cs,max=min (max (0 ,-Pdk),BPc,max) (10)
For k ∈ [1, NΔT], k indicates the time zone of system optimization, vector θbAnd θsFor characterizing microgrid energy-storage system
Buy/sell decision；(9), (10) indicate the BP of microgrid energy-storage system in optimized time zonedbAnd BPcsMaximum value depend on
In load to the net power demand P of energy-storage systemdk。
Further, in the energy of the battery energy storage system and changed power constraint,
Battery energy level constraint is as follows:
Wherein, BE0The battery energy storage system energy level for being time domain when initial；BEminAnd BEmaxRespectively minimum/highest
Boundary；εcAnd εdIt is charge/discharge efficiency；BPlossIndicate energy-storage system wasted power；
Final battery energy level constraint is as follows in time domain:
εc·ΔTT·BPc-εd -1·ΔTT·BPd-BPloss·ΔTT=BEfinal-BE0 (12)
Wherein, BEfinalBattery energy level at the end of desired time domain, value it is horizontal it is ensured that battery energy storage amount not by
It exhausts；
Battery signal is smooth and changed power constraint is as follows:
Due to k ∈ [1, NΔT] and BR >=0, the maximum value of Δ BP expression battery energy storage system power conversion rate, kW/h.
Further, in the micro-grid system signal bondage,
AEMS adjusts microgrid and the public power distribution curve combined at point of power grid, in order to adjust and weaken more than demand base
The peak power of line uses following inequality constraints:
|Dnet|≤|Dhigh-Dbase| (14)
Wherein, DbaseFor the benchmark demand power in system, Dhigh, Dbase>=0, the i.e. absolute value of system net demand power
In the absolute value range of difference between the maximum power value and system benchmark demand power for being more than benchmark demand power；
Power network signal leveling constraint is as follows:
Dmin≤(BPcs+BPcb)-(BPdb+BPds)+Dnet≤Dmax (15)
Wherein, Dmin, DmaxThe respectively minimum value and maximum value of micro-grid system operation power level, the constraint representation microgrid
In system the sum of the charge-discharge electric power of energy-storage battery and the net power demand of system must system can carry minimum and
Between maximum power；
The constraint of grid power signal smoothing is as follows:
Wherein, k ∈ [1, NΔT], GR >=0, the constraint representation is in continuous two time domains, the operation power of micro-grid system
The size of the variable must be in the time domain scale within the variable grade constraint of microgrid power.
Further, chaos is combined to the following improvement of progress with original SOS algorithm: in mutualism and commensalism rank
Chaos local search is used to replace parasitic stages after the iterative process of section, thus enhance the randomness of heuristic search process,
Help to get rid of local optimum.
The present invention has the beneficial effect that: present invention applying rolling time domain Dynamic Control Strategy in model construction is drawn
Local optimum is repeated so that prediction result is more nearly global optimization in the optimization method and concept for entering dynamic prediction；In addition,
Calculate when use improved symbiont searching algorithm, compared with traditional symbiont searching algorithm, convergence rate faster,
And the cost for reaching operational objective in the case where maintaining system normal operating level is minimum, effectively realize improved efficiency and
Cost savings.
Detailed description of the invention
Fig. 1 is that the window of rolling horizon procedure in the specific embodiment of the invention rolls schematic diagram；
Fig. 2 is roll stablized loop model schematic in the specific embodiment of the invention；
Fig. 3 is that PLCM maps distribution map in the specific embodiment of the invention；
Fig. 4 is that the improved symbiont searching algorithm flow chart of chaos optimization is based in the specific embodiment of the invention；
Fig. 5 is microgrid energy-storage system workload demand situation map in application examples of the present invention；
Fig. 6 is microgrid energy-storage system wind-power electricity generation situation map in application examples of the present invention；
Fig. 7 is that microgrid energy-storage system uses tou power price figure in application examples of the present invention；
Fig. 8 is the microgrid signal characteristic figure in application examples of the present invention under different target；
Fig. 9 is that each algorithm calculates effect contrast figure in application examples of the present invention.
Specific embodiment
To further illustrate the technical scheme of the present invention below with reference to the accompanying drawings and specific embodiments.The skill of this field
Art personnel understand the present invention it will be clearly understood that the embodiment described is only to aid in, and should not be regarded as a specific limitation of the invention.
1. microgrid energy-storage system control strategy optimization method
(1) rolling time horizon optimizes
In actual operation, transaction electricity, transaction value is must be taken into consideration in the Optimal Control Strategy of microgrid energy-storage system
And each factor such as operating cost, each factor also change constantly with the difference of operating status, it is therefore desirable to it is pre- to introduce dynamic
The optimization method and concept of survey.The present invention is based on the mixing of roll stablized loop principle building microgrid energy-storage system control strategy is whole
Number linear programming (Mixed Integer Linear Programming, MILP) Optimized model.
Rolling time horizon optimization is a kind of optimization method of dynamic prediction.It is carried out under with probabilistic dynamic environment pre-
When survey, at this moment the global optimization being extremely difficult in ideal just needs that local optimum is repeated so that prediction result is more nearly
Global optimization.In order to achieve the above object, the thought of rolling optimization is introduced in dynamic prediction.It can be with tracking system as one kind
The Optimal Control Strategy of variation, rolling time horizon optimization method can be by constantly pushing optimization collection to roll and use on a timeline
Optimization algorithm solves subproblem, and then reaches local optimum and by continuous scrolling realization finally complete by solving subproblem
Office's optimization.Optimized by rolling time horizon, the run the period of microgrid energy-storage system is divided, and to system in the form of step-length
Operation reserve carry out rolling simulation and optimization.
Rolling window technology is the core of rolling time horizon optimization.As shown in Figure 1, PREkFor k-th of prediction window, DOkIt is complete
Work window, WAITkTo wait window, rolling window time step is Δ T, and prediction window time step is Δ TPRE.System is in Δ
The local optimum rolled in T time, it is assumed that system is reaching local optimum after time step Δ T, then will be all complete
Work task moves into completion window from prediction window, then chooses several tasks from waiting window and enter prediction window, chooses new
The rolling window period starts the rolling optimization of a new round.
(2) the microgrid energy storage system control method based on rolling time horizon optimization
The Controlling model of the following microgrid energy-storage system is based on AEMS (advanced energy management
System it) realizes, in conjunction with roll stablized loop principle, the microgrid energy-storage system Controlling model that the present invention studies is as shown in Figure 2.
Assuming that algorithm prediction generates one and estimates net demand vector power D for each time stepnetPREAnd uncertain error
ΔDnetPRE, wherein DnetPREIndicate prediction power demand and generated output of renewable energy source between difference, kw, this sentence to
The form of amount indicates；DnetPRE=DnetIndicate that the net demand vector power of algorithm prediction is equal to actual power requirement vector, i.e.,
The case where net demand is predicted by perfection, wherein net demand vector power DnetIt indicates between demand power and wind power plant generated output
Practical difference, kw.One will be all formed in each time step in order to simulate the optimal charge and discharge strategy of energy storage device
A MILP problem.Therefore, the present invention attempts to solve the problem using the method for rolling time horizon optimization.Fig. 2, which is described, to be contained
The rolling time horizon optimization method of the micro-grid system of energy storage.Wherein, C is cost (member)；DbaseFor demand power baseline in time domain,
kw；BEfinalFor the time domain end-point prediction energy content of battery, kwh；BE0For time domain starting point actual battery energy, kwh；BPcFor battery storage
Energy system charge power, kw；BPdFor battery energy storage system discharge power, kw.
1) objective function
The optimal objective of setting model is the minimum of microgrid energy-storage system operating cost, then established by the present invention to be based on
The objective function of the microgrid energy-storage system charge/discharge control strategy model of rolling time horizon optimization is as follows:
In formula, the time span of optimized variable is T, and N is rolling time horizon window all during entire model optimization
Quantity, Δ T are the step-length of rolling time horizon window, and the total duration of entire optimization process is NΔT.Reach area within the scope of each step-length
The optimization of next time-domain step size is carried out when domain is optimal.It can be seen that sharing 3 factors in formula (1), meaning is as follows.
Factor 1 indicate system net electric cost, electricity transaction be different from system charge and discharge behavior, energy-storage system into
When row charge and discharge, locating for the microgrid electricity transaction (electricity that microgrid is carried out with other power grids that still can be bought or be sold
Power transaction).Wherein, BPcbIndicate the power in battery energy storage charging purchased from grid parts, kW；Vb TIt is microgrid from power grid
The price of power purchase, member/kW；BPcsIndicate battery energy storage charging when be sold to grid parts power (under the situation, energy storage device
All extra power outputs can not be stored, system is in charging to power grid sale of electricity), kW；Vs TIt is energy-storage system to the valence of power grid sale of electricity
Lattice, member/kW；BPdbWhen for battery discharge energy-storage system purchased from power grid power (under the situation, energy storage system discharges power without
Method meets the difference between workload demand and power output, and system must be from other power grid power purchases), kW；BPdsIt indicates in battery discharge
Energy-storage system is sold to the power of grid parts, kW.It should be noted that working as DnetWhen > 0, BPcs=0 namely when demand excess,
Charge power no longer includes to the part of power grid sale of electricity；Work as DnetWhen < 0, BPdb=0, namely when demand deficiency, discharge power
It no longer include from the part of power grid power purchase.For entire energy-storage system, realtime power is BP=BPcs+BPcb-BPdb-
BPds, BP is that the total charge power of regular representation is greater than discharge power, and system charge is continuously increased, and on the contrary then discharge power, which is greater than, to be filled
Electrical power, system charge are constantly reduced.
The use cost and signal smoothing cost of the expression battery of factor 2.BPcAnd BPdRepresent charge/discharge power, BPc
=BPcs+BPcb, kW；BC is the cost of battery operation, member/kWh；BR is battery energy storage system power ratio in continued time domain step-length
Change magnitude (for measuring battery smoothness), %；CBRFor the cost of smooth power of battery distribution signal, member.
Factor 3 indicates that microgrid signal forms cost.GR is the change magnitude of microgrid power in continued time domain step-length, %；CGR
For the cost of smooth microgrid signal, member；D is the power level of whole system, kW, D=BP+DnetNamely the total electricity of system needs
Seek power；DhighFor the maximum power value more than benchmark demand power, kW；ChighFor the cost for cutting down this power, member；DmaxFor
Microgrid maximum demanded power, kW；DminFor microgrid minimum essential requirement power, kW；CflatFor the cost of smooth microgrid demand power, member.
2) constraint condition
The constraint condition of microgrid and the operation control of battery energy storage device is established in this part, main consideration microgrid energy-storage system
The constraint of battery energy storage device charge/discharge state decision, the energy of battery energy storage system and changed power constraint and micro-grid system
Three parts of signal bondage.
1. battery energy storage device charge/discharge state decision constrains
The variation range constraint of battery charging and discharging power is as follows:
0≤BPc≤BPc,max·α≤BPc,max (2)
0≤BPd≤BPd,max·(1-α)≤BPd,max (3)
Wherein, α ∈ [0,1] is binary vector, for i-th of element, αi=1 indicates charging, αi=0 indicates electric discharge.
BPc,max, BPd,maxThe respectively maximum value of the specified charge/discharge power of energy-storage system, kW.
The state decision constraint that electricity was bought/sold to microgrid from power grid is as follows:
BPdb,max·(1-θb)≤BPdb≤BPdb,max (4)
BPds,max·(1-θs)≤BPds≤BPds,max (5)
BPcs,max·(1-θs)≤BPcs≤BPcs,max (6)
BPcb,max·(1-θb)≤BPcb≤BPcb,max (7)
0≤θs,θb≤1 (8)
For k ∈ [1, NΔT], k indicates the specific time region of system optimization, vector θbAnd θsFor characterizing microgrid energy storage
The buying of system/sell decision.(9) (10) indicate the BP of microgrid energy-storage system in optimized time zonedbAnd BPcsMaximum value
Depending on load to the net power demand P of energy-storage systemdk。
2. the energy of battery energy storage system and changed power constrain
Battery energy level constraint is as follows:
Wherein, BE0The battery energy storage system energy level for being time domain when initial；BEminAnd BEmaxRespectively minimum/highest
Boundary；εcAnd εdIt is charge/discharge efficiency.
Final battery energy level constraint is as follows in time domain:
εc·ΔTT·BPc-εd -1·ΔTT·BPd-BPloss·ΔTT=BEfinal-BE0 (12)
Wherein, BEfinalBattery energy level at the end of desired time domain, value it is horizontal it is ensured that battery energy storage amount not by
It exhausts.
Battery signal is smooth and changed power constraint is as follows:
Due to k ∈ [1, NΔT] and BR >=0, the maximum value of Δ BP expression battery energy storage system power conversion rate, kW/h.It is flat
The purpose of sliding battery power curve is influence of (voltage) transition to energy-storage system in reduction microgrid, otherwise will affect the battery longevity
Life.
3. micro-grid system signal bondage
The adjustable microgrid of AEMS and the public power distribution curve combined at point of power grid.It is more than to need to adjust and weaken
The peak power for seeking baseline uses following inequality constraints:
|Dnet|≤|Dhigh-Dbase| (14)
Wherein, DbaseFor the benchmark demand power in system, Dhigh, Dbase>=0, the i.e. absolute value of system net demand power
In the absolute value range of difference between the maximum power value and system benchmark demand power for being more than benchmark demand power；
Power network signal leveling constraint is as follows:
Dmin≤(BPcs+BPcb)-(BPdb+BPds)+Dnet≤Dmax (15)
Wherein, Dmin, DmaxThe respectively minimum value and maximum value of micro-grid system operation power level, the constraint representation microgrid
In system the sum of the charge-discharge electric power of energy-storage battery and the net power demand of system must system can carry minimum and
Between maximum power；
The constraint of grid power signal smoothing is as follows:
Wherein, k ∈ [1, NΔT], GR >=0, the constraint representation is in continuous two time domains, the operation power of micro-grid system
The size of the variable must be in the time domain scale within the variable grade constraint of microgrid power.
2. improved symbiont searching algorithm (CSOS)
Symbiont searching algorithm (symbiotic organisms search algorithm, SOS) is scholar Cheng
And Prayogo proposed the new meta-heuristic of symbiosis between bion in natural imitation circle a kind of in 2014 and calculates
Method, it is the interaction characteristic in natural imitation circle between different organisms to realize the process for finding optimal value.Under normal conditions,
Symbiosis in nature can be classified as three classes, be mutualism, commensalism, parasitism respectively, and symbiont search is calculated
Method is exactly to imitate these three symbiosis to realize algorithm optimizing.Compared to other meta-heuristic algorithms, symbiont is searched
The feature of rope algorithm maximum is during algorithm optimizing without additional parameter setting, and structure is simple, easy to use and push away
Extensively.But it when solving the problems, such as challenge there is also some, therefore this section proposes CSOS to solve microgrid energy-storage system control
Policy optimization problem processed.
(1) symbiont searching algorithm (SOS)
In symbiont algorithm, bion corresponds to the possibility solution of optimization problem, corresponds to the adaptability of environment suitable
Response function.For symbiont searching algorithm in solving optimization problem, the multiple individuals of random configuration are as the initial of optimization problem
Solution improves ideal adaptation angle value, Jin Erqu by mutualism, commensalism and the parasitic progress information exchange between individual
Obtain the optimal solution of optimization problem.Its main process is as follows:
(a) the mutualism stage
Mutualism refers to that the both sides' biology for establishing symbiosis benefits.The common example of this Relationship Comparison is honeybee
The symbiosis established with flower.According to this rule, shown in the organism of mutualism relationship more new formula following (17):
Wherein, XiAnd XjTwo organisms for establishing symbiosis are respectively indicated, MV indicates the relationship between two organisms.
BF1And BF2It is to benefit the factor, value is 0 or 1, and the interests not phase of mutualism both sides acquisition is embodied with the benefit factor
Together.Xinew、XjnewRespectively indicate updated two organisms.XbestIndicate best bion.
(b) the commensalism stage
Commensalism, which refers in the both sides for establish symbiosis, only has a side to benefit, and harmless to another party.In nature
The more typically symbiosis of fish and shark.According to the rule, the more new formula in commensalism stage is such as shown in (18):
Xinew=Xi+rand(-1,1)×(Xbest-Xj) (18)
Wherein, XiUnilaterally benefit in the relationship of commensalism, and XjIn this connection without aggrieved.
(c) parasitic stages
Parasitism refers to that one side of both sides for establishing symbiosis benefits from this relationship, while another party is in this connection
It is damaged.Common parasitism is malarial mosquito and people in nature.Malarial mosquito benefits from parasitism, the mankind because malarial mosquito and
It comes to harm.
At this stage, XiCertain dimensions are randomly choosed, are replaced with the random value within the scope of search space, are formed artificial
Helminth Parasite_Vector.Then, individual X is randomly choosed in populationj(j ≠ i) compares the fitness of the two
Value retains optimal as new Xj。
As it can be seen that tradition SOS algorithm is when parasitic stages create artificial helminth, randomly selected certain dimensions or selection
Random value within the scope of the search space situation invalid there may be function and effect, leads to the forfeiture of potential solution, falls into
Local optimum influences computational efficiency.Therefore it needs to improve algorithm to improve its performance.
(2) it is based on the improved symbiont searching algorithm of chaos optimization
Traditional SOS algorithm is when parasitic stages create artificial helminth, the search of randomly selected certain dimensions or selection
Random value in the spatial dimension situation invalid there may be function and effect, leads to the forfeiture of potential solution, falls into part
It is optimal, affect computational efficiency.Different from above situation, chaotic maps concentrate on a more promising area in search process
Domain improves the utilization to search space, therefore can quickly find preferable solution.Therefore, the present invention is by chaos
The following improvement of progress is combined with original SOS algorithm: using mixed after the iterative process in mutualism and commensalism stage
Ignorant local search (CLS) is instead of parasitic stages, to enhance the randomness of heuristic search process, helps to get rid of part most
It is excellent.
Since Chaos Search is more effective when small range is searched for, chaotic optimization algorithm of the invention is in radius r model
Enclose interior execution.CLS is only applicable to the best organism X generated after traditional SOS algorithm commensalism stagebest, reason exists
In (Xbest-r,Xbest+ r) it is the maximum local search space of possibility, and can using Chaos Search compared in all stages
Save the more time.Shown in the calculation of initial value such as formula (19) of Chaos Search radius r, and constriction coefficient δ's (0 < δ < 1)
It is gradually reduced under effect.
R=(Xmax-Xmin)/2 (19)
Wherein, XmaxAnd XminIt is search boundary.
Chaos sequence is very sensitive to its initial value, in order to keep the ergodic of Chaos Search, the initial value of chaos sequence
It is generated by random function, as shown in formula (20):
cxk=rand (0,1) (20)
Wherein, cxkIndicate the initial value of chaos sequence.
Next variable cxk+1It is generated by Piecewise linear chaotic map (PLCM), PLCM chaotic maps have controlled statistical special
Property and uniform distribution function, mainly have following advantage: 1. Piecewise linear chaotic map have good ergodic, mixing
Property and certainty；2. Piecewise linear chaotic map has to initial value exquisite sensitivity, good avalanche effect can be generated；3. being segmented
Linear chaotic maps have uniform Invariant Distribution, and resulting chaotic orbit is able to satisfy the balance requirement of cryptographic system.
Shown in PLCM calculation formula such as formula (21):
In formula, xk∈ [0,1) is the original state of chaos system；The control parameter of p ∈ (0,0.5) non-chaos system.
When the number of iterations is 500, the distribution of PLCM chaotic maps is as shown in the figure.
Shown in position such as formula (22) by the optimal bion of PLCM mapping generation:
xk+1=Xbest+r(2cxk+1-1) (22)
Wherein, xk+1It is the position of the optimal bion of kth+1 generation generation of the entire population in CLS, XbestIt is tradition
The position of SOS algorithm optimal bion after mutualism and commensalism phase process calculates xk+1Fitness, if
Its fitness is more excellent, then it is assumed that it is new optimal bion.
The solution process of mentioned CSOS algorithm is as shown in Figure 4.
Application examples
The present invention tests proposed method by certain microgrid energy-storage system, studies system under optimal control policy
Microgrid signal and battery signal feature, and convergence is compared with other algorithms, to verify model and algorithm
Validity.
At one comprising carrying out in the commercial environment with resident, the power generation of system generates electricity application examples from Wind turbine.System
Workload demand situation of uniting is as shown in Figure 5.System wind-power electricity generation situation is as shown in Figure 6.
Micro-grid system uses tou power price, as shown in Figure 7.
The underlying parameter of battery energy storage system is as shown in table 1.
1 micro-grid battery energy storage system parameter list of table
Based on above-mentioned data, microgrid and battery energy storage system power signal characteristic under different target are studied, sets three herein
A target: 1. micro-grid system peak clipping target is cut down the peak value of microgrid demand by energy-storage system under the premise of can cut down, made
It maintains 10kW and following level, at this time peak clipping cost Chigh=1, other costs are 0；2. micro-grid system signal flattens mesh
Mark is promoted the lower limit of microgrid demand using energy-storage system on the basis of to microgrid peak clipping, it is made to maintain 2kW and the above water
It is flat, and be adjusted according to the upper and lower bound of the system net demand of prediction, microgrid signal flattens cost C at this timeflat=1,
He is 0；3. micro-grid system signal smoothing target carries out microgrid signal that is, on the basis of micro-grid system signal flattens target
Smoothing processing, at this point, microgrid signal smoothing cost CGR=Δ T, other costs are 0.Time domain scale be 24 hours, time step to
Amount is set as Δ T=[0.5 0.5 0.5 0.5 0.5 0.5 111222333 3]T。
It is verified for the Optimal Control Model to the microgrid energy-storage system established, the present invention is carried out using CSOS method
It solves, and as a comparison with the optimum results of SOS and POS algorithm.With CSOS to the Optimal Control Strategy of microgrid energy-storage system
When being solved, setting initial population is 100, maximum number of iterations 200.
The microgrid signal characteristic obtained under different target is as shown in Figure 8.Fig. 8 a) embody original microgrid net demand power
Distribution situation.Fig. 8 b) embody microgrid signal characteristic under peak value reduction target.Fig. 8 c) embody microgrid signal leveling target
Under microgrid signal characteristic, to system net demand distribution leveling effect.Fig. 8 d) embody it is micro- under microgrid signal smoothing target
Net signal characteristic realizes the minimum of power swing (black thin indicates upper and lower bound constraint).
On the basis of to the leveling of micro-grid system signal, smoothing processing, while with SOS method and POS method, with research
CSOS algorithm is in convergence and calculates time-related improvement.The micro-grid system operating cost result of calculating is as shown in Figure 9.
It can be seen that CSOS convergence rate faster, and is maintaining system normal operating level compared with SOS, POS
In the case of reach operational objective cost it is minimum, effectively realize improved efficiency and cost savings, demonstrate the present invention established
The scientific section validity based on the microgrid energy-storage system control strategy optimization model for improving symbiont searching algorithm.
Above embodiment is described some details of the invention, but should not be understood as to of the invention
Limitation, those skilled in the art without departing from the principle and spirit of the present invention within the scope of the invention can be right
It is changed, modifies, replacement and variant.
Claims (10)
1. a kind of microgrid energy-storage system control strategy optimization method, which is characterized in that constructed based on roll stablized loop principle micro-
The mixed integer linear programming model of net energy-storage system charge/discharge control strategy, it is minimum with the operating cost of microgrid energy-storage system
Optimal objective is turned to, the constraint condition of microgrid and the operation control of battery energy storage device is established；Using being based on, chaos optimization is improved
Symbiont searching algorithm calculates the model, analyzes the spy of microgrid signal and battery signal under optimal control policy
Sign.
2. microgrid energy-storage system control strategy optimization method according to claim 1, which is characterized in that pass through rolling time horizon
Optimization, divides the run the period of microgrid energy-storage system, and to the operation reserve of microgrid energy-storage system in the form of step-length
Carry out rolling simulation and optimization.
3. microgrid energy-storage system control strategy optimization method according to claim 2, which is characterized in that rolling window technology
It is the core of rolling time horizon optimization, the local optimum that microgrid energy-storage system is rolled in rolling window time step Δ T is false
If microgrid energy-storage system is reaching local optimum after rolling window time step Δ T, then by all completion tasks from prediction
Window moves into completion window, then chooses several tasks from waiting window and enter prediction window, chooses the new rolling window period
Start the rolling optimization of a new round.
4. microgrid energy-storage system control strategy optimization method according to claim 1-3, which is characterized in that roll
Optimization of Time Domain realizes that method is as follows by AEMS: assuming that algorithm prediction generates one and estimates only for each time step
Demand power, while inputting cost, demand power baseline and the actual battery energy of time domain starting point in time domain, thus each
Optimize to obtain the optimal charge-discharge electric power of battery energy storage device by mixed integer linear programming in a time step, and using rolling
Operation reserve of the method for dynamic Optimization of Time Domain to microgrid energy-storage system in the whole service period solves.
5. microgrid energy-storage system control strategy optimization method according to claim 4, which is characterized in that microgrid energy-storage system
The objective function of the mixed integer linear programming model of charge/discharge control strategy are as follows:
In formula, T is the time span of optimized variable, and the quantity of all rolling time horizon windows is N during entire model optimization,
Rolling window time step is Δ T, and the total duration of entire optimization process is NΔT, it is optimal within the scope of each step-length to reach region
When carry out the optimization of next time-domain step size；3 factors are shared in formula (1), meaning is as follows:
Factor 1 indicates the net electric cost of microgrid energy-storage system, BPcbIt indicates in battery energy storage charging purchased from grid parts
Power, kW；For the price of microgrid power purchase from power grid, member/kW；BPcsIt indicates to be sold to grid parts in battery energy storage charging
Power, kW；It is energy-storage system to the price of power grid sale of electricity, member/kW；BPdbEnergy-storage system is purchased from electricity when for battery discharge
The power of net, kW；BPdsExpression energy-storage system in battery discharge is sold to the power of grid parts, kW；It should be noted that working as
Net demand vector power DnetWhen > 0, BPcs=0 namely when demand excess, charge power no longer includes to the portion of power grid sale of electricity
Point；As net demand vector power DnetWhen < 0, BPdb=0, namely when demand deficiency, discharge power no longer includes to purchase from power grid
The part of electricity；For entire energy-storage system, realtime power is BP=BPcs+BPcb-BPdb-BPds, BP is regular representation
Total charge power is greater than discharge power, and system charge is continuously increased, and on the contrary then discharge power is greater than charge power, and system charge is not
It is disconnected to reduce；Net demand vector power DnetIndicate the practical difference between demand power and wind power plant generated output, kw；
Factor 2 indicates the use cost and signal smoothing cost of battery, BPcAnd BPdRepresent charge/discharge power, BPc=BPcs+
BPcb, kW；BC is the cost of battery operation, member/kWh；BR is the knots modification of battery energy storage system power ratio in continued time domain step-length
Grade, %；CBRFor the cost of smooth power of battery distribution signal, member；
Factor 3 indicates that microgrid signal forms cost, and GR is the change magnitude of microgrid power in continued time domain step-length, %；CGRIt is flat
The cost of sliding microgrid signal, member；D is the power level of whole system, kW, D=BP+DnetNamely the total electricity demand function of system
Rate；DhighFor the maximum power value more than benchmark demand power, kW；ChighFor the cost for cutting down this power, member；DmaxFor microgrid
Maximum demanded power, kW；DminFor microgrid minimum essential requirement power, kW；CflatFor the cost of smooth microgrid demand power, member.
6. microgrid energy-storage system control strategy optimization method according to claim 5, which is characterized in that the microgrid and electricity
The constraint condition of pond energy storage device operation control are as follows: the battery energy storage device charge/discharge state decision constraint of microgrid energy-storage system,
The energy and changed power of battery energy storage system constrain and the signal bondage of micro-grid system.
7. microgrid energy-storage system control strategy optimization method according to claim 6, which is characterized in that the battery storage
In energy device charge/discharge state decision constraint,
The variation range constraint of battery charging and discharging power is as follows:
0≤BPc≤BPc,max·α≤BPc,max (2)
0≤BPd≤BPd,max·(1-α)≤BPd,max (3)
Wherein, α ∈ [0,1] is binary vector, for i-th of element, αi=1 indicates charging, αi=0 indicates electric discharge；BPc,max,
BPd,maxThe respectively maximum value of the specified charge/discharge power of energy-storage system, kW；
The state decision constraint that electricity was bought/sold to microgrid from power grid is as follows:
BPdb,max·(1-θb)≤BPdb≤BPdb,max (4)
BPds,max·(1-θs)≤BPds≤BPds,max (5)
BPcs,max·(1-θs)≤BPcs≤BPcs,max (6)
BPcb,max·(1-θb)≤BPcb≤BPcb,max (7)
0≤θs,θb≤1 (8)
For k ∈ [1, NΔT], k indicates the time zone of system optimization, vector θbAnd θsFor characterize the buying of microgrid energy-storage system/
Sell decision；(9), (10) indicate the BP of microgrid energy-storage system in optimized time zonedbAnd BPcsMaximum value depend on it is negative
Net power demand P of the lotus to energy-storage systemdk。
8. microgrid energy-storage system control strategy optimization method according to claim 7, which is characterized in that the battery energy storage
In energy and the changed power constraint of system,
Battery energy level constraint is as follows:
Wherein, BE0The battery energy storage system energy level for being time domain when initial；BEminAnd BEmaxRespectively minimum/highest boundary；
εcAnd εdIt is charge/discharge efficiency；BPlossIndicate energy-storage system wasted power；
Final battery energy level constraint is as follows in time domain:
εc·ΔTT·BPc-εd -1·ΔTT·BPd-BPloss·ΔTT=BEfinal-BE0 (12)
Wherein, BEfinalIt is battery energy level at the end of desired time domain, value level is not it is ensured that battery energy storage amount is consumed
To the greatest extent；
Battery signal is smooth and changed power constraint is as follows:
Due to k ∈ [1, NΔT] and BR >=0, the maximum value of Δ BP expression battery energy storage system power conversion rate, kW/h.
9. microgrid energy-storage system control strategy optimization method according to claim 6, which is characterized in that the micro-grid system
In signal bondage,
AEMS adjusts microgrid and the public power distribution curve combined at point of power grid, in order to adjust and weaken more than demand baseline
Peak power uses following inequality constraints:
|Dnet|≤|Dhigh-Dbase| (14)
Wherein, DbaseFor the benchmark demand power in system, Dhigh, Dbase>=0, i.e., the absolute value of system net demand power is super
It crosses in the absolute value range of the difference between the maximum power value of benchmark demand power and system benchmark demand power；
Power network signal leveling constraint is as follows:
Dmin≤(BPcs+BPcb)-(BPdb+BPds)+Dnet≤Dmax (15)
Wherein, Dmin, DmaxThe respectively minimum value and maximum value of micro-grid system operation power level, the constraint representation micro-grid system
The minimum and maximum that the sum of charge-discharge electric power and the net power demand of system of interior energy-storage battery must can be carried in system
Between power；
The constraint of grid power signal smoothing is as follows:
Wherein, k ∈ [1, NΔT], GR >=0, the constraint representation is in continuous two time domains, the change of the operation power of micro-grid system
The size of amount must be in the time domain scale within the variable grade constraint of microgrid power.
10. microgrid energy-storage system control strategy optimization method according to claim 5, which is characterized in that by chaos and original
Beginning SOS algorithm combines the following improvement of progress: using chaos office after the iterative process in mutualism and commensalism stage
Portion's search replaces parasitic stages to help to get rid of local optimum to enhance the randomness of heuristic search process.
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Application publication date: 20190830 |
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